Email this article 
Open Access
Subscription or Fee Access
Survey on Brain Tumour Detection and Classification Using Image Processing
Abstract
Abstract---Medical Image Processing is a complex and challenging field nowadays. Processing of MRI images is one of the parts of this field. Automated and accurate classification of MR brain images is of importance for the analysis and interpretation of these images and many methods have been proposed Region growing is an important application of image segmentation in medical research for detection of tumour. In this an effective modified region growing technique for detection of brain tumour is used. Modified region growing includes an orientation constraint in addition to the normal intensity constrain. The performance of the proposed technique is systematically evaluated using the MRI brain images database. For validating the effectiveness of the modified region growing, the quantity rate parameter detection, the sensitivity, specificity and accuracy values will be used. Comparative analyses will be made for the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network.
Keywords
References
A.R.Kavitha,Dr.C.Chellamuthu, Ms.Kavin Rupa, “An Efficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRI Images,”IEEE, 2012.
H. B. Kekre, Tanuja Sarode, Kavita Raut, ''Detection of Tumor in MRI Using Vector Quantization Segmentation", International Journal of Engineering Science andnTechnology, Vol. 2, No: 8, pp: 3753-3757,2010.
Jue Wu, Albert C.S. Chung, "A novel framework for segmentation of deep brain structures based on Markov dependence tree", Neuro Image, Elsevier, vol: 46, pp: 1027-1036,2009.
S. Klein, et al. "Early diagnosis of dementia based on intersubject whole-brain dissimilarities," Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE Press April 2010, pp. 249-252, doi: 10.1109/ISBI.2010.5490366.
E. A. El-Dahshan, T. Hosny, and A. M. Salem, "Hybrid intelligent techniques for MRI brain images classification," Journal of Digital Signal Processing, vol. 20(2), March 2010, pp. 433-441, doi: 10.1016/j.dsp.2009.07.002.
S. Chaplot, L. Patnaik, and N. Jagannathan, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,"Biomedical Signal Processing and Control, vol. 1(1), 2006, pp. 86-92.
Mussarat Yasmin, Muhammad Sharif and Sajjad Mohsin” Neural Networks in Medical Imaging Applications: A Survey” World Applied Sciences Journal 22 (1): 85-96, 2013
Mohd Fauzi Othman and Mohd Ariffanan Mohd Basri, “Probabilistic Neural Network for Brain Tumor Classification,” Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, ISMS.2011.32, 2011. (IEEE Transaction).
Salim Lahmiri and Mounir Boukadoum,et.al., “Classification of Brain MRI using the LH and HL Wavelet Transform Sub-bands,” IEEE, 2011. (IEEE Transaction).
Ahmed kharrat, Karim Gasmi, et.al, “A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine,” Leonardo Journal of Sciences, pp.71-82, 2010. (Journal)
Ahmed Kharrat, Mohamed Ben Messaoud, et.al, “Detection of Brain Tumor in Medical Images,” International Conference on Signals, Circuits and Systems IEEE, pp.1-6, 2009. (IEEE Transactions)
Dr. Samir Kumar Bandyopadhyay, “Detection of Brain Tumor-A Proposed Method,”Journal of Global Research in Computer Science, Volume 2, No. 1, January 2011.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.